Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …

More than 3.5 billion people live in rural areas, where water and water energy resources
play an important role in ensuring sustainable and productive rural economies. This article …

Reservoir computing is a best-in-class machine learning algorithm for processing
information generated by dynamical systems using observed time-series data. Importantly, it …

In recent years, machine-learning techniques, particularly deep learning, have outperformed
traditional time-series forecasting approaches in many contexts, including univariate and …

We introduce a probability distribution, combined with an efficient sampling algorithm, for
weights and biases of fully-connected neural networks. In a supervised learning context, no …

We articulate the design imperatives for machine learning based digital twins for nonlinear
dynamical systems, which can be used to monitor the “health” of the system and anticipate …

Nonlinear tracking control enabling a dynamical system to track a desired trajectory is
fundamental to robotics, serving a wide range of civil and defense applications. In control …

Reservoir computing (RC), a particular form of recurrent neural network, is under explosive
development due to its exceptional efficacy and high performance in reconstruction and/or …

Reservoir computers are powerful machine learning algorithms for predicting nonlinear
systems. Unlike traditional feedforward neural networks, they work on small training data …

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural
network in which neurons are randomly connected. Once initialized, the connection …